Fix 1/N floor in effective-rank collapse score after mean-centering

monai/metrics/embedding_collapse.py

@@ -295,7 +295,10 @@ def _effective_rank_score(emb: torch.Tensor) -> torch.Tensor:
     probs = sv / sv_sum
     safe_probs = probs.clamp_min(torch.finfo(probs.dtype).tiny)
     eff_rank: torch.Tensor = torch.exp(-(probs * safe_probs.log()).sum())
-    max_rank: torch.Tensor = emb.new_tensor(float(min(emb.shape[0], emb.shape[1])))
+    # Mean-centering above forces the rows to sum to zero, a linear dependency, so
+    # rank(centered) <= min(N - 1, D). Normalizing by min(N, D) would impose a 1/N
+    # floor on the score whenever N <= D. No-op when N > D: both expressions equal D.
+    max_rank: torch.Tensor = emb.new_tensor(float(min(emb.shape[0] - 1, emb.shape[1])))
     return (emb.new_tensor(1.0) - eff_rank / max_rank).clamp(0.0, 1.0)
 
 

tests/metrics/test_embedding_collapse.py

@@ -224,10 +224,31 @@ def test_formula_matches_manual(self):
         probs = sv / sv_sum
         safe_probs = probs.clamp_min(torch.finfo(probs.dtype).tiny)
         eff_rank = (-(probs * safe_probs.log()).sum()).exp()
-        expected = (1.0 - eff_rank / min(20, 16)).clamp(0.0, 1.0)
+        expected = (1.0 - eff_rank / min(20 - 1, 16)).clamp(0.0, 1.0)
         score = _effective_rank_score(emb)
         self.assertAlmostEqual(float(score), float(expected), places=5)
 
+    def test_effective_rank_no_collapse_when_n_leq_d(self):
+        # Isotropic embeddings have NO dimensional collapse -> score must be ~0.
+        # RED on dev: returns ~0.25 (the 1/N floor). GREEN after fix: ~0.0004.
+        torch.manual_seed(0)
+        score = _effective_rank_score(torch.randn(4, 768))
+        self.assertLess(float(score), 0.05)
+
+    def test_effective_rank_two_distinct_samples_report_no_collapse(self):
+        # Two maximally-spread points span the only subspace 2 samples CAN span.
+        # RED on dev: returns exactly 0.5 ("50% collapsed"). GREEN after fix: 0.0.
+        emb = torch.zeros(2, 768)
+        emb[0, 0] = 1.0
+        emb[1, 0] = -1.0
+        self.assertAlmostEqual(float(_effective_rank_score(emb)), 0.0, places=5)
+
+    def test_effective_rank_unchanged_when_n_gt_d(self):
+        # Regression guard: the fix MUST be a no-op where the code is already correct.
+        torch.manual_seed(0)
+        score = _effective_rank_score(torch.randn(1024, 768))
+        self.assertAlmostEqual(float(score), 0.1234, places=3)
+
 
 class TestPerClassRank(unittest.TestCase):
     def test_keys_present_for_each_class(self):